Overview

Dataset statistics

Number of variables20
Number of observations25201
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 MiB
Average record size in memory160.0 B

Variable types

NUM15
CAT5

Warnings

offerId has a high cardinality: 336 distinct values High cardinality
date has a high cardinality: 138 distinct values High cardinality
Consumo_Whatsapp is highly correlated with consumo_INTERNET and 5 other fieldsHigh correlation
consumo_INTERNET is highly correlated with Consumo_Whatsapp and 6 other fieldsHigh correlation
Consumo_Facebook is highly correlated with consumo_INTERNET and 5 other fieldsHigh correlation
Consumo_Twitter is highly correlated with consumo_INTERNET and 5 other fieldsHigh correlation
Consumo_Snapchat is highly correlated with consumo_INTERNET and 6 other fieldsHigh correlation
Consumo_Youtube is highly correlated with consumo_INTERNET and 6 other fieldsHigh correlation
Consumo_EasyTaxi is highly correlated with consumo_INTERNET and 6 other fieldsHigh correlation
Consumo_AppTaxi is highly correlated with consumo_INTERNET and 4 other fieldsHigh correlation
Consumo_Waze is highly correlated with Consumo_AppTaxiHigh correlation
Ingreso has 13213 (52.4%) zeros Zeros
Cantidad_Vendidos has 13213 (52.4%) zeros Zeros
consumo_INTERNET has 4983 (19.8%) zeros Zeros
Consumo_Whatsapp has 5548 (22.0%) zeros Zeros
Consumo_Facebook has 6566 (26.1%) zeros Zeros
Consumo_Twitter has 9177 (36.4%) zeros Zeros
Consumo_Instagram has 5843 (23.2%) zeros Zeros
Consumo_Snapchat has 6822 (27.1%) zeros Zeros
Consumo_Youtube has 5362 (21.3%) zeros Zeros
Consumo_ClaroVideo has 16387 (65.0%) zeros Zeros
Consumo_Waze has 8250 (32.7%) zeros Zeros
Consumo_GoogleMaps has 13934 (55.3%) zeros Zeros
Consumo_EasyTaxi has 6872 (27.3%) zeros Zeros
Consumo_AppTaxi has 11259 (44.7%) zeros Zeros
Consumo_WAP has 13203 (52.4%) zeros Zeros

Reproduction

Analysis started2020-11-18 20:12:25.509237
Analysis finished2020-11-18 20:13:02.367994
Duration36.86 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

offerId
Categorical

HIGH CARDINALITY

Distinct336
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
31351
 
138
31533
 
138
31316
 
138
31266
 
138
31358
 
138
Other values (331)
24511 
ValueCountFrequency (%) 
313511380.5%
 
315331380.5%
 
313161380.5%
 
312661380.5%
 
313581380.5%
 
312411380.5%
 
312391380.5%
 
313191380.5%
 
313571380.5%
 
312171380.5%
 
Other values (326)2382194.5%
 
2020-11-18T15:13:02.516597image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique51 ?
Unique (%)0.2%
2020-11-18T15:13:02.653231image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length5
Mean length5
Min length5

date
Categorical

HIGH CARDINALITY

Distinct138
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
2020-07-14
 
211
2020-07-02
 
210
2020-07-03
 
210
2020-07-04
 
207
2020-07-01
 
207
Other values (133)
24156 
ValueCountFrequency (%) 
2020-07-142110.8%
 
2020-07-022100.8%
 
2020-07-032100.8%
 
2020-07-042070.8%
 
2020-07-012070.8%
 
2020-07-152030.8%
 
2020-07-162020.8%
 
2020-07-102020.8%
 
2020-06-302020.8%
 
2020-07-232020.8%
 
Other values (128)2314591.8%
 
2020-11-18T15:13:02.772951image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-18T15:13:02.882004image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

nombre_dia
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
viernes
3691 
martes
3666 
jueves
3665 
miércoles
3660 
sábado
3615 
Other values (2)
6904 
ValueCountFrequency (%) 
viernes369114.6%
 
martes366614.5%
 
jueves366514.5%
 
miércoles366014.5%
 
sábado361514.3%
 
lunes345713.7%
 
domingo344713.7%
 
2020-11-18T15:13:02.982734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-18T15:13:03.068500image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:13:03.182199image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length6
Mean length6.581762628
Min length5

tipo_dia
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
HABIL
17046 
FIN_DE_SEMANA
7062 
FESTIVO
 
1093
ValueCountFrequency (%) 
HABIL1704667.6%
 
FIN_DE_SEMANA706228.0%
 
FESTIVO10934.3%
 
2020-11-18T15:13:03.288916image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-18T15:13:03.364675image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:13:03.452477image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length5
Mean length7.328558391
Min length5

Ingreso
Real number (ℝ≥0)

ZEROS

Distinct5842
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4556884.453
Minimum0
Maximum182298000
Zeros13213
Zeros (%)52.4%
Memory size196.9 KiB
2020-11-18T15:13:03.582252image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3816000
95-th percentile29186300
Maximum182298000
Range182298000
Interquartile range (IQR)816000

Descriptive statistics

Standard deviation16811146.36
Coefficient of variation (CV)3.689175473
Kurtosis37.99869354
Mean4556884.453
Median Absolute Deviation (MAD)0
Skewness5.789316577
Sum1.148380451e+11
Variance2.826146419e+14
MonotocityNot monotonic
2020-11-18T15:13:03.721917image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01321352.4%
 
600001100.4%
 
170001080.4%
 
300001050.4%
 
9500960.4%
 
20000870.3%
 
80000670.3%
 
40000600.2%
 
50000600.2%
 
10000590.2%
 
Other values (5832)1123644.6%
 
ValueCountFrequency (%) 
01321352.4%
 
900190.1%
 
10008< 0.1%
 
16005< 0.1%
 
1700210.1%
 
ValueCountFrequency (%) 
1822980001< 0.1%
 
1779562501< 0.1%
 
1742917501< 0.1%
 
1741287501< 0.1%
 
1662282501< 0.1%
 

Cantidad_Vendidos
Real number (ℝ≥0)

ZEROS

Distinct3129
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1498.575731
Minimum0
Maximum182799
Zeros13213
Zeros (%)52.4%
Memory size196.9 KiB
2020-11-18T15:13:03.861137image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q397
95-th percentile4279
Maximum182799
Range182799
Interquartile range (IQR)97

Descriptive statistics

Standard deviation11210.23419
Coefficient of variation (CV)7.48059238
Kurtosis162.0626281
Mean1498.575731
Median Absolute Deviation (MAD)0
Skewness12.45769169
Sum37765607
Variance125669350.7
MonotocityNot monotonic
2020-11-18T15:13:03.990790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01321352.4%
 
19994.0%
 
25722.3%
 
34291.7%
 
43241.3%
 
52641.0%
 
62551.0%
 
72080.8%
 
81960.8%
 
91740.7%
 
Other values (3119)856734.0%
 
ValueCountFrequency (%) 
01321352.4%
 
19994.0%
 
25722.3%
 
34291.7%
 
43241.3%
 
ValueCountFrequency (%) 
1827991< 0.1%
 
1784191< 0.1%
 
1747171< 0.1%
 
1746231< 0.1%
 
1666821< 0.1%
 

consumo_INTERNET
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct20198
Distinct (%)80.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.149422216e+11
Minimum0
Maximum5.76149308e+13
Zeros4983
Zeros (%)19.8%
Memory size196.9 KiB
2020-11-18T15:13:04.137358image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118204204
median5831551735
Q32.4532175e+11
95-th percentile3.577409196e+12
Maximum5.76149308e+13
Range5.76149308e+13
Interquartile range (IQR)2.453035458e+11

Descriptive statistics

Standard deviation3.710091319e+12
Coefficient of variation (CV)4.552582037
Kurtosis89.39333933
Mean8.149422216e+11
Median Absolute Deviation (MAD)5831551735
Skewness8.874770133
Sum2.053735893e+16
Variance1.37647776e+25
MonotocityNot monotonic
2020-11-18T15:13:04.262583image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0498319.8%
 
1048576011< 0.1%
 
805< 0.1%
 
136314884< 0.1%
 
403< 0.1%
 
2002< 0.1%
 
2042< 0.1%
 
1609176381< 0.1%
 
3.149746927e+111< 0.1%
 
1.302834924e+111< 0.1%
 
Other values (20188)2018880.1%
 
ValueCountFrequency (%) 
0498319.8%
 
403< 0.1%
 
805< 0.1%
 
881< 0.1%
 
1241< 0.1%
 
ValueCountFrequency (%) 
5.76149308e+131< 0.1%
 
5.632914298e+131< 0.1%
 
5.615546493e+131< 0.1%
 
5.550616848e+131< 0.1%
 
5.530537581e+131< 0.1%
 

Consumo_Whatsapp
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct19634
Distinct (%)77.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.175032089e+12
Minimum0
Maximum9.905134795e+13
Zeros5548
Zeros (%)22.0%
Memory size196.9 KiB
2020-11-18T15:13:04.404204image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1668746
median1371334710
Q31.04484506e+11
95-th percentile4.063270383e+12
Maximum9.905134795e+13
Range9.905134795e+13
Interquartile range (IQR)1.044838372e+11

Descriptive statistics

Standard deviation7.196434276e+12
Coefficient of variation (CV)6.12445766
Kurtosis107.6974455
Mean1.175032089e+12
Median Absolute Deviation (MAD)1371334710
Skewness9.956420079
Sum2.961198367e+16
Variance5.178866629e+25
MonotocityNot monotonic
2020-11-18T15:13:04.553802image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0554822.0%
 
925< 0.1%
 
1004< 0.1%
 
1404< 0.1%
 
3003< 0.1%
 
6003< 0.1%
 
2003< 0.1%
 
1312< 0.1%
 
1822< 0.1%
 
7322< 0.1%
 
Other values (19624)1962577.9%
 
ValueCountFrequency (%) 
0554822.0%
 
521< 0.1%
 
601< 0.1%
 
641< 0.1%
 
722< 0.1%
 
ValueCountFrequency (%) 
9.905134795e+131< 0.1%
 
9.323800956e+131< 0.1%
 
9.294599094e+131< 0.1%
 
9.222377706e+131< 0.1%
 
9.199120887e+131< 0.1%
 

Consumo_Facebook
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct18613
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.115066802e+11
Minimum0
Maximum6.585966701e+13
Zeros6566
Zeros (%)26.1%
Memory size196.9 KiB
2020-11-18T15:13:04.688446image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median437487596
Q36.49769224e+10
95-th percentile2.805649067e+12
Maximum6.585966701e+13
Range6.585966701e+13
Interquartile range (IQR)6.49769224e+10

Descriptive statistics

Standard deviation5.021016255e+12
Coefficient of variation (CV)6.187276553
Kurtosis108.8422632
Mean8.115066802e+11
Median Absolute Deviation (MAD)437487596
Skewness10.00119909
Sum2.045077985e+16
Variance2.521060423e+25
MonotocityNot monotonic
2020-11-18T15:13:04.813112image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0656626.1%
 
1408< 0.1%
 
837< 0.1%
 
1237< 0.1%
 
1003< 0.1%
 
1562< 0.1%
 
4162< 0.1%
 
14987414421< 0.1%
 
328547321< 0.1%
 
84108306851< 0.1%
 
Other values (18603)1860373.8%
 
ValueCountFrequency (%) 
0656626.1%
 
521< 0.1%
 
837< 0.1%
 
851< 0.1%
 
911< 0.1%
 
ValueCountFrequency (%) 
6.585966701e+131< 0.1%
 
6.568429131e+131< 0.1%
 
6.450909325e+131< 0.1%
 
6.400277228e+131< 0.1%
 
6.370785669e+131< 0.1%
 

Consumo_Twitter
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct16010
Distinct (%)63.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.269205419e+10
Minimum0
Maximum8.516344811e+11
Zeros9177
Zeros (%)36.4%
Memory size196.9 KiB
2020-11-18T15:13:04.950746image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6226951
Q31506193481
95-th percentile5.010569501e+10
Maximum8.516344811e+11
Range8.516344811e+11
Interquartile range (IQR)1506193481

Descriptive statistics

Standard deviation6.188478745e+10
Coefficient of variation (CV)4.875868519
Kurtosis79.17630055
Mean1.269205419e+10
Median Absolute Deviation (MAD)6226951
Skewness8.381967652
Sum3.198524576e+14
Variance3.829726918e+21
MonotocityNot monotonic
2020-11-18T15:13:05.083391image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0917736.4%
 
525< 0.1%
 
403< 0.1%
 
18932< 0.1%
 
10322< 0.1%
 
68632< 0.1%
 
5162< 0.1%
 
1035972< 0.1%
 
1562< 0.1%
 
67022< 0.1%
 
Other values (16000)1600263.5%
 
ValueCountFrequency (%) 
0917736.4%
 
403< 0.1%
 
525< 0.1%
 
921< 0.1%
 
1041< 0.1%
 
ValueCountFrequency (%) 
8.516344811e+111< 0.1%
 
8.18138203e+111< 0.1%
 
8.071451134e+111< 0.1%
 
7.649479749e+111< 0.1%
 
7.486271521e+111< 0.1%
 

Consumo_Instagram
Real number (ℝ≥0)

ZEROS

Distinct19171
Distinct (%)76.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.283511164e+10
Minimum0
Maximum3.113866053e+12
Zeros5843
Zeros (%)23.2%
Memory size196.9 KiB
2020-11-18T15:13:05.216172image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12156
median515242637
Q31.555578486e+10
95-th percentile2.146358378e+11
Maximum3.113866053e+12
Range3.113866053e+12
Interquartile range (IQR)1.55557827e+10

Descriptive statistics

Standard deviation1.656015394e+11
Coefficient of variation (CV)3.866023295
Kurtosis102.3082374
Mean4.283511164e+10
Median Absolute Deviation (MAD)515242637
Skewness8.73464198
Sum1.079487649e+15
Variance2.742386987e+22
MonotocityNot monotonic
2020-11-18T15:13:05.340534image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0584323.2%
 
156200.1%
 
52200.1%
 
104150.1%
 
16012< 0.1%
 
3289< 0.1%
 
3128< 0.1%
 
408< 0.1%
 
6005< 0.1%
 
805< 0.1%
 
Other values (19161)1925676.4%
 
ValueCountFrequency (%) 
0584323.2%
 
408< 0.1%
 
52200.1%
 
805< 0.1%
 
921< 0.1%
 
ValueCountFrequency (%) 
3.113866053e+121< 0.1%
 
3.063069444e+121< 0.1%
 
3.057935483e+121< 0.1%
 
3.050319775e+121< 0.1%
 
3.047374712e+121< 0.1%
 

Consumo_Snapchat
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct18351
Distinct (%)72.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.020771098e+10
Minimum0
Maximum1.141482018e+12
Zeros6822
Zeros (%)27.1%
Memory size196.9 KiB
2020-11-18T15:13:05.477167image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median27316968
Q32722185806
95-th percentile3.521415969e+10
Maximum1.141482018e+12
Range1.141482018e+12
Interquartile range (IQR)2722185806

Descriptive statistics

Standard deviation5.459373046e+10
Coefficient of variation (CV)5.34828333
Kurtosis118.3467875
Mean1.020771098e+10
Median Absolute Deviation (MAD)27316968
Skewness10.13656925
Sum2.572445244e+14
Variance2.980475405e+21
MonotocityNot monotonic
2020-11-18T15:13:05.619746image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0682227.1%
 
1648< 0.1%
 
1606< 0.1%
 
87513< 0.1%
 
76622< 0.1%
 
2602< 0.1%
 
91962< 0.1%
 
82332< 0.1%
 
922< 0.1%
 
89642< 0.1%
 
Other values (18341)1835072.8%
 
ValueCountFrequency (%) 
0682227.1%
 
521< 0.1%
 
601< 0.1%
 
922< 0.1%
 
1481< 0.1%
 
ValueCountFrequency (%) 
1.141482018e+121< 0.1%
 
1.031159791e+121< 0.1%
 
9.220100903e+111< 0.1%
 
9.063954726e+111< 0.1%
 
8.875949683e+111< 0.1%
 

Consumo_Youtube
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct19833
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.66812601e+11
Minimum0
Maximum1.365943073e+13
Zeros5362
Zeros (%)21.3%
Memory size196.9 KiB
2020-11-18T15:13:05.759374image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1815253
median1007872659
Q34.749104581e+10
95-th percentile7.230210308e+11
Maximum1.365943073e+13
Range1.365943073e+13
Interquartile range (IQR)4.749023056e+10

Descriptive statistics

Standard deviation7.74191479e+11
Coefficient of variation (CV)4.641085112
Kurtosis90.0787624
Mean1.66812601e+11
Median Absolute Deviation (MAD)1007872659
Skewness8.848367947
Sum4.203844358e+15
Variance5.993724461e+23
MonotocityNot monotonic
2020-11-18T15:13:05.888067image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0536221.3%
 
3124< 0.1%
 
1842< 0.1%
 
522< 0.1%
 
9362< 0.1%
 
16642< 0.1%
 
2.535759659e+101< 0.1%
 
24614876491< 0.1%
 
1145225221< 0.1%
 
8.703340529e+111< 0.1%
 
Other values (19823)1982378.7%
 
ValueCountFrequency (%) 
0536221.3%
 
522< 0.1%
 
801< 0.1%
 
921< 0.1%
 
1041< 0.1%
 
ValueCountFrequency (%) 
1.365943073e+131< 0.1%
 
1.283554961e+131< 0.1%
 
1.247020397e+131< 0.1%
 
1.220196996e+131< 0.1%
 
1.171719435e+131< 0.1%
 

Consumo_ClaroVideo
Real number (ℝ≥0)

ZEROS

Distinct8806
Distinct (%)34.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90658378.86
Minimum0
Maximum1.533600248e+10
Zeros16387
Zeros (%)65.0%
Memory size196.9 KiB
2020-11-18T15:13:06.025661image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32977659
95-th percentile381944436
Maximum1.533600248e+10
Range1.533600248e+10
Interquartile range (IQR)2977659

Descriptive statistics

Standard deviation477701475.3
Coefficient of variation (CV)5.269247932
Kurtosis219.6370926
Mean90658378.86
Median Absolute Deviation (MAD)0
Skewness11.8993386
Sum2.284681806e+12
Variance2.281986995e+17
MonotocityNot monotonic
2020-11-18T15:13:06.162334image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01638765.0%
 
106503< 0.1%
 
7202< 0.1%
 
76052< 0.1%
 
2032882< 0.1%
 
78102< 0.1%
 
65602< 0.1%
 
131932< 0.1%
 
86072< 0.1%
 
342423621< 0.1%
 
Other values (8796)879634.9%
 
ValueCountFrequency (%) 
01638765.0%
 
751< 0.1%
 
1201< 0.1%
 
2151< 0.1%
 
2841< 0.1%
 
ValueCountFrequency (%) 
1.533600248e+101< 0.1%
 
1.486600442e+101< 0.1%
 
1.335056394e+101< 0.1%
 
1.1859468e+101< 0.1%
 
1.176412408e+101< 0.1%
 

Consumo_Waze
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct15698
Distinct (%)62.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean861557196.4
Minimum0
Maximum4.154746844e+10
Zeros8250
Zeros (%)32.7%
Memory size196.9 KiB
2020-11-18T15:13:06.316920image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5026322
Q3302628206
95-th percentile3938346587
Maximum4.154746844e+10
Range4.154746844e+10
Interquartile range (IQR)302628206

Descriptive statistics

Standard deviation3332973023
Coefficient of variation (CV)3.868545277
Kurtosis51.82127639
Mean861557196.4
Median Absolute Deviation (MAD)5026322
Skewness6.750533539
Sum2.171210291e+13
Variance1.110870917e+19
MonotocityNot monotonic
2020-11-18T15:13:06.445577image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0825032.7%
 
1561530.6%
 
521080.4%
 
312570.2%
 
208570.2%
 
104420.2%
 
40300.1%
 
260270.1%
 
364260.1%
 
468230.1%
 
Other values (15688)1642865.2%
 
ValueCountFrequency (%) 
0825032.7%
 
40300.1%
 
521080.4%
 
602< 0.1%
 
641< 0.1%
 
ValueCountFrequency (%) 
4.154746844e+101< 0.1%
 
4.044547932e+101< 0.1%
 
3.997553241e+101< 0.1%
 
3.960271963e+101< 0.1%
 
3.9143091e+101< 0.1%
 

Consumo_GoogleMaps
Real number (ℝ≥0)

ZEROS

Distinct11192
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean177399647.1
Minimum0
Maximum3.600313541e+10
Zeros13934
Zeros (%)55.3%
Memory size196.9 KiB
2020-11-18T15:13:06.592822image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33656263
95-th percentile403485114
Maximum3.600313541e+10
Range3.600313541e+10
Interquartile range (IQR)3656263

Descriptive statistics

Standard deviation1506970310
Coefficient of variation (CV)8.494776256
Kurtosis320.2045106
Mean177399647.1
Median Absolute Deviation (MAD)0
Skewness16.71641689
Sum4.470648506e+12
Variance2.270959515e+18
MonotocityNot monotonic
2020-11-18T15:13:07.026641image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01393455.3%
 
731911< 0.1%
 
73187< 0.1%
 
57826< 0.1%
 
154174< 0.1%
 
73174< 0.1%
 
73154< 0.1%
 
73164< 0.1%
 
153143< 0.1%
 
151633< 0.1%
 
Other values (11182)1122144.5%
 
ValueCountFrequency (%) 
01393455.3%
 
4031< 0.1%
 
6061< 0.1%
 
8011< 0.1%
 
11281< 0.1%
 
ValueCountFrequency (%) 
3.600313541e+101< 0.1%
 
3.563798115e+101< 0.1%
 
3.557363042e+101< 0.1%
 
3.526826998e+101< 0.1%
 
3.448581619e+101< 0.1%
 

Consumo_EasyTaxi
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct18324
Distinct (%)72.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.469547251e+10
Minimum0
Maximum2.916639372e+12
Zeros6872
Zeros (%)27.3%
Memory size196.9 KiB
2020-11-18T15:13:07.169298image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median45765657
Q35547475425
95-th percentile1.23384902e+11
Maximum2.916639372e+12
Range2.916639372e+12
Interquartile range (IQR)5547475425

Descriptive statistics

Standard deviation1.913196221e+11
Coefficient of variation (CV)5.514253252
Kurtosis103.6573874
Mean3.469547251e+10
Median Absolute Deviation (MAD)45765657
Skewness9.680682163
Sum8.743606027e+14
Variance3.660319781e+22
MonotocityNot monotonic
2020-11-18T15:13:07.299089image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0687227.3%
 
1842< 0.1%
 
4682< 0.1%
 
922< 0.1%
 
1042< 0.1%
 
303440972< 0.1%
 
1241872< 0.1%
 
75111271< 0.1%
 
6466681< 0.1%
 
18612314721< 0.1%
 
Other values (18314)1831472.7%
 
ValueCountFrequency (%) 
0687227.3%
 
521< 0.1%
 
922< 0.1%
 
1042< 0.1%
 
1441< 0.1%
 
ValueCountFrequency (%) 
2.916639372e+121< 0.1%
 
2.902233558e+121< 0.1%
 
2.878352815e+121< 0.1%
 
2.843574441e+121< 0.1%
 
2.832398475e+121< 0.1%
 

Consumo_AppTaxi
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct13839
Distinct (%)54.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120566342.1
Minimum0
Maximum8348715360
Zeros11259
Zeros (%)44.7%
Memory size196.9 KiB
2020-11-18T15:13:07.437727image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median27306
Q317077595
95-th percentile448068672
Maximum8348715360
Range8348715360
Interquartile range (IQR)17077595

Descriptive statistics

Standard deviation577436442.7
Coefficient of variation (CV)4.789366857
Kurtosis71.76271319
Mean120566342.1
Median Absolute Deviation (MAD)27306
Skewness7.93140042
Sum3.038392386e+12
Variance3.334328454e+17
MonotocityNot monotonic
2020-11-18T15:13:07.562526image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01125944.7%
 
924< 0.1%
 
83453< 0.1%
 
84583< 0.1%
 
85633< 0.1%
 
79873< 0.1%
 
85613< 0.1%
 
82903< 0.1%
 
82833< 0.1%
 
94893< 0.1%
 
Other values (13829)1391455.2%
 
ValueCountFrequency (%) 
01125944.7%
 
521< 0.1%
 
924< 0.1%
 
1041< 0.1%
 
1641< 0.1%
 
ValueCountFrequency (%) 
83487153601< 0.1%
 
79796410881< 0.1%
 
79703153361< 0.1%
 
78450984421< 0.1%
 
77284021071< 0.1%
 

Consumo_WAP
Real number (ℝ≥0)

ZEROS

Distinct11750
Distinct (%)46.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1602938207
Minimum0
Maximum3.331323471e+11
Zeros13203
Zeros (%)52.4%
Memory size196.9 KiB
2020-11-18T15:13:07.696141image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31608872
95-th percentile3531833017
Maximum3.331323471e+11
Range3.331323471e+11
Interquartile range (IQR)1608872

Descriptive statistics

Standard deviation1.087102085e+10
Coefficient of variation (CV)6.78193383
Kurtosis228.0689592
Mean1602938207
Median Absolute Deviation (MAD)0
Skewness12.95406314
Sum4.039564575e+13
Variance1.181790944e+20
MonotocityNot monotonic
2020-11-18T15:13:07.850394image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01320352.4%
 
25637< 0.1%
 
23886< 0.1%
 
23976< 0.1%
 
23865< 0.1%
 
23554< 0.1%
 
23914< 0.1%
 
23954< 0.1%
 
23754< 0.1%
 
24344< 0.1%
 
Other values (11740)1195447.4%
 
ValueCountFrequency (%) 
01320352.4%
 
402< 0.1%
 
801< 0.1%
 
1562< 0.1%
 
1841< 0.1%
 
ValueCountFrequency (%) 
3.331323471e+111< 0.1%
 
2.763256606e+111< 0.1%
 
2.679384817e+111< 0.1%
 
2.663883824e+111< 0.1%
 
2.510851598e+111< 0.1%
 

tipoPaquete
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
Datos
16875 
Todo Incluido
8130 
Hajj
 
193
Welcome Package
 
3
ValueCountFrequency (%) 
Datos1687567.0%
 
Todo Incluido813032.3%
 
Hajj1930.8%
 
Welcome Package3< 0.1%
 
2020-11-18T15:13:07.980045image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-18T15:13:08.053681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:13:08.142447image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length5
Mean length7.574381969
Min length4

Interactions

2020-11-18T15:12:33.014159image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:33.171824image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:33.315402image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:33.453056image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:33.594677image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:33.716388image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:33.838212image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:33.978836image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:34.101506image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:34.236148image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:34.367858image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:34.485547image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:34.629124image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:34.758822image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:34.872471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:34.995144image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:35.118719image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:35.244385image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:35.363750image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:35.484428image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:35.609141image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:35.811554image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:35.932281image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:36.061508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:36.188611image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:36.316484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:36.456097image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:36.591733image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:36.718914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:36.851581image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:36.977243image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:37.111396image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:37.240049image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:37.365716image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:37.483446image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:37.599137image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:37.716814image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:37.839040image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:37.959312image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:38.084933image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:38.219571image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:38.330352image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:38.449040image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:38.575652image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:38.703310image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:38.837950image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:38.961202image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:39.085865image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:39.216552image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:39.347194image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:39.477879image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:39.591573image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:39.710258image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:39.841445image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:39.964116image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:40.086801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:40.306215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:40.425895image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:40.547570image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:40.667763image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:40.799430image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:40.932100image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:41.057764image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:41.172976image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:41.298677image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:41.414332image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:41.542988image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:41.676631image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:41.808279image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:41.943960image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:42.067585image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:42.179286image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:42.298966image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:42.420641image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:42.537363image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:42.655605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:42.767641image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:42.879349image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:42.992003image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:43.101708image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:43.211415image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:43.319127image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:43.430828image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:43.537589image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:43.650242image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:43.771917image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:43.875639image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:43.988859image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:44.107561image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:44.220259image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:44.330963image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:44.456628image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:44.585514image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:44.704189image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:44.826002image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:44.946742image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:45.061169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:45.178865image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:45.298500image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:45.557808image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:45.692447image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-11-18T15:12:46.562376image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:46.676077image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:46.788784image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:46.901479image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:47.011177image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:47.126832image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:47.240573image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:47.359253image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:47.481928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:47.592632image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:47.710316image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:47.827009image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:47.936711image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:48.053354image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:48.182010image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:48.308712image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:48.429349image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:48.554061image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:48.673788image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:48.791466image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:48.913857image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:49.035629image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:49.160758image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:49.290412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:49.406102image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:49.534760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:49.673389image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:49.793573image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:49.916284image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:50.045899image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:50.182578image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:50.328146image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:50.468770image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:50.610435image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:50.730117image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:50.868701image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:50.996397image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:51.127242image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:51.261836image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:51.386028image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:51.517230image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:51.643479image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:51.767141image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:51.894800image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:52.164084image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:52.277784image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:52.396422image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:52.516776image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:52.629443image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:52.742812image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:52.855510image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:52.971750image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:53.094421image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:53.216146image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:53.332857image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:53.460516image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:53.579214image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:53.695900image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:53.810594image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:53.941255image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:54.076930image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:54.208420image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:54.344026image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:54.486645image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:54.622281image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:54.762906image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:54.884238image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:55.013892image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:55.144067image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:55.271762image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:55.396438image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:55.520109image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:55.643733image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:55.769395image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:55.892067image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:56.021722image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:56.152371image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:56.270057image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:56.388740image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:56.502482image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:56.642063image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:56.766730image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:56.891396image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:57.017132image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:57.130847image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:57.258453image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:57.379129image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:57.497813image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:57.628996image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:57.756655image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:57.872883image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:57.982590image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:58.092297image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:58.203002image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:58.312814image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:58.423473image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:58.535206image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:58.649899image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:58.766588image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:58.872306image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:58.992041image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:59.107732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:59.215458image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:59.334138image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:59.456810image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:59.580527image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:59.710158image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:59.827843image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:12:59.945100image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:13:00.261752image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:13:00.388634image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:13:00.505323image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:13:00.628005image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:13:00.757615image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:13:00.872817image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:13:01.007459image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:13:01.136624image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:13:01.254333image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-11-18T15:13:08.253149image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-18T15:13:08.489401image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-18T15:13:08.719059image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-18T15:13:08.952165image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-11-18T15:13:09.174395image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-11-18T15:13:01.582546image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T15:13:02.081293image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

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Last rows

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25192316062020-10-31sábadoFIN_DE_SEMANA420000.028.02.920223e+101.787290e+101.080013e+101.659305e+091.685854e+093.192981e+083.715575e+09595711.089592693.01493322.01.100810e+0917657747.00.0Todo Incluido
25193316082020-10-31sábadoFIN_DE_SEMANA0.00.03.039198e+097.939348e+084.734440e+082.677778e+081.669336e+081.498436e+071.195365e+080.012130962.0151226.06.294502e+07349042.00.0Todo Incluido
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25197316162020-10-31sábadoFIN_DE_SEMANA60000.01.09.569780e+093.826572e+098.119840e+088.067161e+074.482064e+084.613674e+074.385330e+090.04043.01200526.01.312763e+0867332.00.0Todo Incluido
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